'''simple docstring'''




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)


__UpperCAmelCase					="https://openaipublic.azureedge.net/jukebox/models/"
__UpperCAmelCase					={
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[int]:
    if key.endswith('''.model.1.bias'''							) and len(key.split('''.'''							)							) > 10:
        __lowerCamelCase            =    key.replace('''.model.1.bias'''							,    '''.conv1d_1.bias'''							)
    elif key.endswith('''.model.1.weight'''							) and len(key.split('''.'''							)							) > 10:
        __lowerCamelCase            =    key.replace('''.model.1.weight'''							,    '''.conv1d_1.weight'''							)
    elif key.endswith('''.model.3.bias'''							) and len(key.split('''.'''							)							) > 10:
        __lowerCamelCase            =    key.replace('''.model.3.bias'''							,    '''.conv1d_2.bias'''							)
    elif key.endswith('''.model.3.weight'''							) and len(key.split('''.'''							)							) > 10:
        __lowerCamelCase            =    key.replace('''.model.3.weight'''							,    '''.conv1d_2.weight'''							)

    if "conditioner_blocks.0." in key:
        __lowerCamelCase            =    key.replace('''conditioner_blocks.0'''							,    '''conditioner_blocks'''							)

    if "prime_prior" in key:
        __lowerCamelCase            =    key.replace('''prime_prior'''							,    '''encoder'''							)

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        __lowerCamelCase            =    key.replace('''.emb.'''							,    '''.'''							)

    if key.endswith('''k'''							):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace('''.k'''							,    '''.codebook'''							)
    if "y_emb." in key:
        return key.replace('''y_emb.'''							,    '''metadata_embedding.'''							)

    if "x_emb.emb." in key:
        __lowerCamelCase            =    key.replace('''0.x_emb.emb'''							,    '''embed_tokens'''							)

    if "prime_state_ln" in key:
        return key.replace('''prime_state_ln'''							,    '''encoder.final_layer_norm'''							)
    if ".ln" in key:
        return key.replace('''.ln'''							,    '''.layer_norm'''							)
    if "_ln" in key:
        return key.replace('''_ln'''							,    '''_layer_norm'''							)

    if "prime_state_proj" in key:
        return key.replace('''prime_state_proj'''							,    '''encoder.proj_in'''							)
    if "prime_x_out" in key:
        return key.replace('''prime_x_out'''							,    '''encoder.lm_head'''							)
    if "prior.x_out" in key:
        return key.replace('''x_out'''							,    '''fc_proj_out'''							)
    if "x_emb" in key:
        return key.replace('''x_emb'''							,    '''embed_tokens'''							)

    return key






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    {}
    import re

    __lowerCamelCase            =    re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(
        r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

    __lowerCamelCase            =    re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(
        r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

    __lowerCamelCase            =    re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(
        r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
    __lowerCamelCase            =    re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''							)

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_encoder_block_conv_in.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[2]							) * 2 + int(groups[3]							)
            __lowerCamelCase            =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
            __lowerCamelCase            =    re_encoder_block_conv_in.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_encoder_block_resnet.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_encoder_block_resnet.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[2]							) * 2 + int(groups[3]							)
            __lowerCamelCase            =    {'''1''': 1, '''3''': 2}[groups[-2]]
            __lowerCamelCase            =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
            __lowerCamelCase            =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            __lowerCamelCase            =    prefix + resnet_block
            __lowerCamelCase            =    re_encoder_block_resnet.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_encoder_block_proj_out.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            __lowerCamelCase            =    re_encoder_block_proj_out.sub(UpperCamelCase__							,    UpperCamelCase__							)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_decoder_block_conv_out.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[2]							) * 2 + int(groups[3]							) - 2
            __lowerCamelCase            =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
            __lowerCamelCase            =    re_decoder_block_conv_out.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_decoder_block_resnet.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_decoder_block_resnet.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[2]							) * 2 + int(groups[3]							) - 2
            __lowerCamelCase            =    {'''1''': 1, '''3''': 2}[groups[-2]]
            __lowerCamelCase            =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
            __lowerCamelCase            =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            __lowerCamelCase            =    prefix + resnet_block
            __lowerCamelCase            =    re_decoder_block_resnet.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_decoder_block_proj_in.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
            __lowerCamelCase            =    re_decoder_block_proj_in.sub(UpperCamelCase__							,    UpperCamelCase__							)

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_prior_cond_conv_out.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[1]							) * 2 + int(groups[2]							) - 2
            __lowerCamelCase            =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
            __lowerCamelCase            =    re_prior_cond_conv_out.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_prior_cond_resnet.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_prior_cond_resnet.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    int(groups[1]							) * 2 + int(groups[2]							) - 2
            __lowerCamelCase            =    {'''1''': 1, '''3''': 2}[groups[-2]]
            __lowerCamelCase            =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
            __lowerCamelCase            =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            __lowerCamelCase            =    prefix + resnet_block
            __lowerCamelCase            =    re_prior_cond_resnet.sub(UpperCamelCase__							,    UpperCamelCase__							)

        elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__							):
            __lowerCamelCase            =    re_prior_cond_proj_in.match(UpperCamelCase__							)
            __lowerCamelCase            =    regex_match.groups()
            __lowerCamelCase            =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            __lowerCamelCase            =    re_prior_cond_proj_in.sub(UpperCamelCase__							,    UpperCamelCase__							)

        # keep original key
        else:
            __lowerCamelCase            =    original_key

        __lowerCamelCase            =    replace_key(UpperCamelCase__							)

        if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
            print(f"""failed converting {original_key} to {key}, does not match"""							)

        # handle missmatched shape
        elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
            __lowerCamelCase            =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""							)
            __lowerCamelCase            =    original_key

        __lowerCamelCase            =    original_key
        __lowerCamelCase            =    value

    return new_dict






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__=None							,    UpperCamelCase__=None							)      ->       Any:
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'							)[-1]}"""							):
            __lowerCamelCase            =    requests.get(f"""{PREFIX}{file}"""							,    allow_redirects=UpperCamelCase__							)
            os.makedirs(f"""{pytorch_dump_folder_path}/"""							,    exist_ok=UpperCamelCase__							)
            open(f"""{pytorch_dump_folder_path}/{file.split('/'							)[-1]}"""							,    '''wb'''							).write(r.content							)

    __lowerCamelCase            =    MODEL_MAPPING[model_name.split('''/'''							)[-1]]

    __lowerCamelCase            =    JukeboxConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    JukeboxModel(UpperCamelCase__							)

    __lowerCamelCase            =    []
    __lowerCamelCase            =    {}
    for i, dict_name in enumerate(UpperCamelCase__							):
        __lowerCamelCase            =    torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'							)[-1]}"""							)['''model''']

        __lowerCamelCase            =    {}
        for k in old_dic.keys():
            if k.endswith('''.b'''							):
                __lowerCamelCase            =    old_dic[k]
            elif k.endswith('''.w'''							):
                __lowerCamelCase            =    old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                __lowerCamelCase            =    old_dic[k]
            else:
                __lowerCamelCase            =    old_dic[k]

        __lowerCamelCase            =    '''vqvae''' if i == 0 else f"""priors.{3 - i}"""
        __lowerCamelCase            =    fix_jukebox_keys(UpperCamelCase__							,    model.state_dict()							,    UpperCamelCase__							,    UpperCamelCase__							)
        weight_dict.append(UpperCamelCase__							)

    __lowerCamelCase            =    weight_dict.pop(0							)
    model.vqvae.load_state_dict(UpperCamelCase__							)
    for i in range(len(UpperCamelCase__							)							):
        model.priors[i].load_state_dict(weight_dict[2 - i]							)

    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    with open(f"""{pytorch_dump_folder_path}/mapping.json"""							,    '''w'''							) as txtfile:
        json.dump(UpperCamelCase__							,    UpperCamelCase__							)

    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(UpperCamelCase__							)

    return weight_dict


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  __UpperCAmelCase					=parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					={
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["EfficientNetImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientNetForImageClassification",
      "EfficientNetModel",
      "EfficientNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure)
